Extreme dependence for multivariate data

Orthant tail dependence of multivariate extreme value distributions

AMS 2000 subject classifications: 62H20 62P05 Keywords: Tail dependence Heavy tails Copula Multivariate extreme value distribution Marshall–Olkin distribution Archimedean copula Contagion risk a b s t r a c t The orthant tail dependence describes the relative deviation of upper-(or lower-) orthant tail probabilities of a random vector from similar orthant tail probabilities of a subset of its c...

Dependence Structures for Multivariate High–Frequency Data in Finance

Stylised facts for univariate high–frequency data in finance are well–known. They include scaling behaviour, volatility clustering, heavy tails, and seasonalities. The multivariate problem, however, has scarcely been addressed up to now. In this paper, bivariate series of high–frequency FX spot data for major FX markets are investigated. First, as an indispensable prerequisite for further analy...

Prioritization method for non-extreme ecient unitsin data envelopment analysis

Super eciency data envelopment analysis(DEA) model can be used in ranking the performanceof ecient decision making units(DMUs). In DEA, non-extreme ecient unitshave a super eciency score one and the existing super eciency DEA models do notprovide a complete ranking about these units. In this paper, we will propose a methodfor ranking the performance of the extreme and non-extreme ecient units.

Extreme Sample Censoring Problems with Multivariate Data - I

A conditional approach for multivariate extreme values

Multivariate extreme value theory and methods concern the characterization, estimation and extrapolation of the joint tail of the distribution of a d-dimensional random variable. Existing approaches are based on limiting arguments in which all components of the variable become large at the same rate.This limit approach is inappropriate when the extreme values of all the variables are unlikely t...